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Sources of long-livedatmospheric VOCs atthe rural boreal
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Atmos. Chem. Phys. Discuss., 15, 1459314641,
2015www.atmos-chem-phys-discuss.net/15/14593/2015/doi:10.5194/acpd-15-14593-2015
Author(s) 2015. CC Attribution 3.0 License.
This discussion paper is/has been under review for the journal
Atmospheric Chemistryand Physics (ACP). Please refer to the
corresponding final paper in ACP if available.
Sources of long-lived atmospheric VOCsat the rural boreal forest
site, SMEAR IIJ. Patokoski1, T. M. Ruuskanen1, M. K. Kajos1, R.
Taipale1, P. Rantala1, J. Aalto2,T. Ryypp3, T. Nieminen1,4, H.
Hakola5, and J. Rinne1,5,6
1Division of Atmospheric Sciences, Department of Physics,
University of Helsinki, P.O. Box 64,00014 University of Helsinki,
Finland2Department of Forest Sciences, University of Helsinki,
Finland3Finnish Meteorological Institute, Arctic Research Centre,
Thtelntie 62, 99600 Sodankyl,Finland4Helsinki Institute of Physics,
P.O. Box 64, 00014 University of Helsinki, Finland5Finnish
Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland6Department of Geosciences and Geography, University of
Helsinki, Finland
Received: 19 February 2015 Accepted: 26 April 2015 Published: 26
May 2015
Correspondence to: J. Patokoski
([email protected])
Published by Copernicus Publications on behalf of the European
Geosciences Union.
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Abstract
In this study a long-term volatile organic compounds (VOCs) data
set, measured at theSMEAR II (Station for measuring
EcosystemAtmosphere Relations) boreal forest siteat Hyytil, Finland
during the years 20062011, was investigated. VOC mixing ratioswere
measured using proton transfer reaction mass spectrometry. Four-day
backward5trajectories and the Unmix 6.0 receptor model were used
for source area and sourcecomposition analysis. Two major forest
fire events, one in Eastern Europe and one inRussia, took place
during the measurement period. The effect of these fires was
clearlyvisible in the trajectory analysis, lending confidence to
the method employed with thisdata set. Elevated volume mixing
ratios (VMRs) of non-biogenic VOCs, e.g. acetonitrile10and aromatic
VOCs, related to forest fires were observed. Ten major source areas
forlong-lived VOCs (methanol, acetonitrile, acetaldehyde, acetone,
benzene and toluene)were identified at the SMEAR II site. The main
source areas for all the targeted VOCswere Western Russia, Northern
Poland, Kaliningrad and Baltic countries. Industrialareas in
Northern Continental Europe were also found to be source areas for
certain15VOCs. Both trajectory and receptor analysis showed that
air masses from NorthernFennoscandia were less polluted with both
the VOCs studied and with other tracegases (CO, SO2 and NOx) than
areas of Eastern and Western Continental Europe,Western Russia and
Southern Fennoscandia.
1 Introduction20
Volatile organic compounds (VOCs) in the atmosphere have several
sources, both bio-genic and anthropogenic. On a global scale the
emissions of biogenic VOCs are esti-mated to be an order of
magnitude higher than anthropogenic ones (Guenther et al.,1995).
The main biogenic sources are forests (Simpson et al., 1999), and
to a lesserdegree crops (Guenther et al., 1995) and algae in
aquatic ecosystems (Fink et al.,252007). However, in the northern
latitude winter and during air pollution events, anthro-
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pogenic emissions can be dominant. The most important
non-biogenic VOC sourcesare traffic, biomass burning (especially
forest fires), the extraction and refining of fossilfuels, and the
evaporation of solvents (Blake et al., 2009). Once emitted, the
VOCsmay be transported and oxidized in photochemical processes
during this transport.The main oxidants for VOCs in the atmosphere
are ozone (O3), and the hydroxyl (OH)5and nitrate (NO3) radicals
(Atkinson and Arey, 2003). Depending on their reactivity withthese
oxidants, some VOCs have a relatively long lifetime from a few days
to a fewmonths, and can be transported over long distances. In
order to study this atmospherictransport and to identify the source
areas of the measured concentrations, trajectoryanalysis has been
widely used (Stohl, 1996; Stohl and Seibert, 1998).10
In source areas, VOCs are emitted, for example, from forest
fires (de Gouw et al.,2006) or industrial sources or, on the other
hand, from densely-populated urban areas:these have a mixture of
various source elements such as industry, power plants andvehicles
(Baker et al., 2008). The locations of source areas may vary
temporally andspatially, due to e.g. seasonal variations in the
biogenic activity of plants or variations15in anthropogenic
activity or meteorological conditions.
The SMEAR II (Station for measuring EcosystemAtmosphere
Relations) site, lo-cated in a rural environment in boreal forest
in Southern Finland, has been used fortwo decades to investigate
atmospheric processes leading to aerosol particle forma-tion and
growth. There is ample evidence that biogenic VOCs contribute to
these20processes at this site (e.g. Tunved et al., 2006; Ehn et
al., 2014). However, there isalso evidence that emissions and
atmospheric concentrations of some VOCs, suchas monoterpenes, are
occasionally affected by anthropogenic processes (Liao et al.,2011;
Haapanala et al., 2012). Many VOCs with a relatively long lifetime
have bothbiogenic and anthropogenic sources. These VOCs include
e.g. methanol, acetone and25acetaldehyde which lifetimes for
Southern Finland have been estimated to be in spring16, 33 and 4
days, respectively by Patokoski et al. (2014). As these compounds
can betransported over thousands of kilometres, their atmospheric
concentrations observedat SMEAR II are likely to be influenced by
distant anthropogenic emissions in addition
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to more local sources. Previously, Ruuskanen et al. (2009) have
observed that thereis an indication of long-range transport of VOCs
from continental Europe to Finland.Helln et al. (2006) studied the
source areas of VOCs in Helsinki urban air, and foundout that most
of the benzene was from distant sources. During long-range
transport,the VOCs in air masses coming to the SMEAR II site are
often at least partly oxidized.5The observed VOCs may thus have
both primary and secondary sources.
The volume mixing ratios (VMRs) of VOCs have earlier been
studied at SMEAR IIe.g. by Rinne et al. (2005); Ruuskanen et al.
(2009); Hakola et al. (2009, 2012) andHelln et al. (2004). These
studies have only made use of short data sets, with theexception of
that by Hakola et al. (2009, 2012), thus not allowing a study of
annual to10inter-annual variations. Hakola et al. (2012) made
continuous measurements using anin situ gas-chromatograph and
Hakola et al. (2009) using noon-time samples and labo-ratory
analysis, but in both measured only terpenoids. The analysis of
source areas hasnot been possible without employing larger data
sets comprising several compounds.
Thus our aim in this study is to use VOC VMR data covering
several years (2006152011) and to investigate the source profiles
and source areas of relatively long-lifetimeVOCs (methanol,
acetaldehyde, acetone, toluene, benzene) observed at the SMEAR
IIsite. Winters are characterized by stronger anthropogenic
influence e.g. from heatingwhereas biogenic activity is more
pronounced in the summer. The specific aims of thisstudy are (1) to
investigate long term changes in sources affecting the VOC
concentra-20tions and to quantify their trends (biogenic and
anthropogenic) over a six-year period,(2) to determine the biogenic
vs. the anthropogenic influence from defining the sourceprofiles of
VOCs in relation to other trace gases for SMEAR II, (3) to identify
the sourceareas of VOCs in air masses arriving from Northern
Fennoscandia, Northern Conti-nental Europe and Western Russia
within the lifetime of the long-lived VOCs, such as25acetone and
benzene, observed at SMEAR II, (4) to investigate how these
sourcescoincide with e.g. wildfires and biomass burning, and major
urban and industrial areas.The SMEAR IIs focus is atmosphere
biosphere interaction and define aerosol forma-tion and growth
processes in boreal climate zone. While many studies have focused
on
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the influence of local to regional sources on the observed trace
gases concentrations(Patokoski et al., 2014; Liao et al., 2011;
Eerdekens et al., 2009; Hakola et al., 2009,2012), this study aims
at identifying regional to continental source areas and focuseson
characterizing the effect of long range transport.
2 Methods5
2.1 Measurements site
VOCs VMRs were measured at the SMEAR II site in Finland during
the years 20062011. SMEAR II is a rural measurement station located
in boreal forest at Hyytil,Southern Finland (6151N, 2417 E, 180
ma.s.l.). A detailed description of the siteis given by e.g. Hari
and Kulmala (2005). The site is located 220 km north-west
of10Helsinki and 60 km north-east of Tampere which with a
population of about 200 000is the largest city near the site.
Continuous long-term measurements of trace gases,aerosol particles
and gas exchange between the atmosphere and the biosphere havebeen
carried out at SMEAR II since the mid-1990s (Vesala et al., 1998).
The forestsurrounding the station is dominated by Scots pine (Pinus
sylvestris), sown in 196215(Bck et al., 2012). There is also some
Norway spruce (Picea abies), aspen (Populustremula) and birch
(Betula sp.) at the site (Hari and Kulmala, 2005). Within a square
of40km40 km centred on the station 23 % of the area is covered by
pine forests, 26 %by spruce forest, and 21 % by mixed forest
(Haapanala et al., 2007). Agriculture andwater bodies cover 10 and
13 %, respectively.20
2.2 Instrumentation and sampling
The VOC VMRs were measured with a quadrupole proton transfer
reaction mass spec-trometer (PTR-MS, Ionicon Analytik GmbH,
Austria, Lindinger et al., 1998a). PTR-MSuses the hydronium ion
(H3O
+) as a primary reactant ion. VOCs with a larger pro-ton
affinity than that of water will readily react with H3O
+ (Lindinger et al., 1998a,2514597
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b). The PTR-MS uses a soft ionization technique and most
compounds hardly frag-ment at all (Tani et al., 2003). The reactant
and the product ions are mass filtered witha quadrupole mass
spectrometer and detected with a secondary electron
multiplier(SEM). Product ions are protonated and thus e.g. methanol
is identified at molecularmass m 33. During the measurements, the
PTR-MS was calibrated with a VOC calibra-5tion gas mixture at
regular weekly or biweekly intervals. During the period
20062011four different VOC mixtures, which all included 1618 VOCs
and were all manufac-tured by Apel-Riemer Environmental Inc., USA,
were used. A detailed description ofthe calibration procedure and
the VMR calculation methodology is presented by Taipaleet al.
(2008). The following settings were used during the measurements:
Udrift varied10from 450 to 525 V (mean value 479 V), temperature of
drift tube was 50 C, E/N rangevaried from 105 to 130 Td (mean value
110 Td) and normalized sensitivity for e.g. ace-tone varied from 18
to 40 ncpsppb1v (mean value 28 ncpsppb
1v ). Detection limits for
studied VOCs are presented in Table 1. The instrument was
calibrated every time whensettings were changed, taking into
account the changes in the sensitivity and cancelling15effects on
the measured VMRs.
Seven masses: m 33, m 42, m 45, m 59, m 79, m 93 and m 137
a.m.u, which havebeen identified as methanol, acetonitrile,
acetaldehyde, acetone, benzene, toluene andmonoterpenes,
respectively (de Gouw and Warneke, 2007), were measured. PTR-MSand
GC-MS concentration measurements in Hyytil have agreed well for
monoter-20penes (Ruuskanen et al., 2005) as well as of methanol,
acetaldehyde, acetone, ben-zene and toluene (Kajos et al., 2015).
These VOCs, excluding monoterpenes, haverelatively long lifetimes,
and the behaviour of their VMRs was investigated with trajec-tory
analysis and the Unmix 6.0 receptor model. One-hour median mixing
ratios werecalculated for the analysis. All the data are presented
in local standard time (UTC +2 h).25There are some gaps in the data
due to maintenance, some technical problems andthe usage of the
PTR-MS in other measurement campaigns (Fig. 1). Data quality
waschecked and data were filtered by removing values below the
detection limit. The de-tection limits for the measurement periods
are presented in Table 1.
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During the years 20062009 the VMRs of the VOCs were measured
from a scaf-folding tower at a height of 22 m above the ground.
From summer 2006 to spring 2007the measurement sequence consisted
of one-hour VMR measurements followed byone-hour disjunct eddy
covariance measurements (Rinne et al., 2007). Thus VMR datawere
obtained every second hour. The sampling protocol changed in March
2007 when5a chamber with a Scots pine shoot enclosed was included
in the measurement cycle;VOC VMRs were then measured every third
hour instead of every second hour. InMay 2010 the sampling protocol
changed again when the instrument was transportedto another
measurement hut. At the same time the sampling inlet was moved
about50 m to another tower, 33.6 m above ground. These measurement
heights were cho-10sen for analysis because they are more
representative for depicting concentrationsdue to transport, rather
than concentrations inside the canopy. The canopy height wasabout
16 m.
Nitrogen oxides (NOx), carbon monoxide (CO), sulphur dioxide
(SO2) and ozone(O3) mixing ratio data were used in the analysis as
ancillary data. The mixing ratios15of NOx were measured with a
chemiluminescence technique (TEI 42C TL, ThermoEnvironmental
Instruments, MA, USA) and CO was measured by an infrared light
ab-sorption analyzer (HORIBA APMA 360, Horiba, Japan). SO2 was
measured with a flu-orescence analyzer (TEI 43 BS, Thermo
Environmental Instruments, MA, USA) andO3 by an ultraviolet light
absorption technique (TEI 49, Thermo Environmental Instru-20ments,
MA, USA). CO, NOx, O3 and SO2 were also measured at a height of 33
mexcept in 2010, when CO was measured at 16.8 m.
2.3 Trajectory analysis
HYSPLIT 4 (HYbrid Single Particle Lagrangian Intergrated
Trajectory) was used forair mass trajectories (Draxler and Hess,
1998). The arrival height of the calculated25trajectories at SMEAR
II was 100 ma.g.l., thus representing air masses arriving in
thesurface layer, in which the VOC VMR measurements were made.
Backward trajectoriesof 96 h (4 days) were calculated for every
hour from 2006 to 2011. For the purposes
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of the trajectory analysis the VOC VMR data were interpolated
using a Piecewise Cu-bic Hermite Interpolating Polynomial (PCHIP)
to cover every hour and thus match thetrajectory data. Each time
measured VOC VMR data were available at SMEAR II, theywere
associated with a trajectory arriving at the site at the same time.
The path of theback trajectories was considered with a 1 1 spatial
resolution. The VMRs were as-5sumed to remain constant during the
whole transport time. The grid cells over whichtrajectories
traversed prior to the observation of high VMR values at SMEAR II
wereassociated with high values in the source field. Thus the
trajectory maps are interpretedso that there is a color code
corresponding to VMRs e.g. if there is an area in a mapwhere
methanol is observed to be 3 ppbv this means that the measured
value 3 ppbv10at the site is assumed to come from that area (see
Figs. 35). The trajectory analysiswas limited to the area between
50 and 75N in latitude; 12 and 50 E in longitude.For reasons of
statistical significance, at least 25 trajectories had to cross a
grid cellin order for that grid cell to be accepted into the
analysis, i.e., grid cells with less than25 traverses were omitted
from the analysis. Finally, all the VOC VMRs at each grid15cell
were averaged to yield the VOC source field (Stohl et al., 1995;
Stohl and Seibert,1998).
2.4 Forest fire locations from satellite observations
The forest fire location data are obtained from FIRMS (Fire
Information Resource Man-agements System), which delivers fire
locations and hotspots as globally observed by20MODIS (Moderate
Resolution Imaging Spectroradiometer). Data have been collectedby
NASAs Earth observing system (EOS) Terra and Aqua satellites. With
these twosatellites global data coverage is achieved every 1 to 2
days.
2.5 Unmix 6.0
Source compositions and contributions were investigated with the
multivariate receptor25model EPA Unmix 6.0 (Norris et al., 2007),
developed by Ronald Henry at the Univer-
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sity of Southern California. A basic problem of multivariate
receptor models is how todetermine the optimal number of sources,
the source fingerprints and their contribu-tion from the ambient
air VOC measurement data alone. Some additional constraintsmust be
added in order to obtain unique solutions. In Unmix, the
composition and con-tribution of the sources must be non-negative.
In addition to this, Unmix searches for5periods when the data
indicate that the contribution of one of the sources is
missingcompletely or its contribution is minor. The application of
Unmix to VOC data obtainedby gas chromatographic methods at an
urban site in Helsinki is described by Hellnet al. (2003).
According to the recommendations for the model, the regression of
eachof the species explained by the sources (R2) should be over
0.8, while the signal-to-10noise ratio should be over 2. In this
study, Unmix was applied for inorganic trace gasand VOC data,
except monoterpenes that were excluded from the source area
trajec-tory analysis (see Sect. 3.5). One-hour medians of trace
gases and VOCs were usedas input data. Data were filtered by
horizontal wind speed, excluding from analysis ob-servations (30 %
of data were excluded from analysis) with wind speed below 1
ms1.15All the results exceeded the recommended R2 and
signal-to-noise values, indicatingreceptor modelling results that
were applicable and valid.
3 Results and discussion
The VOC VMRs of most compounds which were studied have maxima in
summertimeand minima in winter (Fig. 1). However, benzene behaves
in the opposite way. This is20due to a lack of significant biogenic
sources of benzene and its shorter atmosphericlifetime in summer.
Of the other studied VOCs, methanol, acetone, acetaldehyde andthe
monoterpenes also have biogenic sources around the measurement site
(Rinneet al., 2007), and acetonitrile is emitted by biomass
burning.
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3.1 Lifetimes of the observed VOCs
To estimate the VOC chemical lifetimes (e-folding times), OH and
NO3 radical concen-trations, were estimated, based on data from
Hakola et al. (2003). The summertimeOH concentration presented by
Hakola et al. (2003) agreed well with the observations(Rinne et
al., 2012), except in the case of summertime NO3, whose radical
concentra-5tion is at least twice as high as the observations.
Thus, for this study, the annual cycleof NO3 as presented by Hakola
et al. (2003) was scaled by dividing it by two. Summer-and
wintertime median O3 concentrations were calculated from O3
measurements atSMEAR II. Photolysis values for summer- and
wintertime were calculated followingHelln et al. (2004). Actinic
flux values corresponding to the albedo for snow-covered10forest in
winter ( = 0.8) were used when photolysis values were calculated.
The con-centrations of oxidants and reaction rate coefficients are
presented in Tables 2 and3.
The calculated atmospheric lifetimes of the studied VOCs
(methanol, acetaldehyde,acetone, benzene and toluene) in summer-
and wintertime are presented in Table 4.15Compared to the lifetimes
of monoterpenes (about one day in winter and one hourin summer)
these lifetimes were much longer. For most compounds, the
atmosphericlifetimes exceed the duration of the back-trajectories
used in this analysis. However, insummertime both toluene and
acetaldehyde have a lifetime below four days. Thus, forthese
compounds, the results of the four-day backward trajectory analysis
should be20interpreted with caution.
The following sections describe the source areas of the studied
VOCs: (1) duringlong-range transport episodes from forest fires,
(2) for all VOC VMR data and (3) forsummers and winters
separately.
3.2 Forest fire episodes in Russia in the summers of 2006 and
201025
During the measurement periods two particularly active forest
fire episodes with severalfire hotspots occurred in Russia, one in
summer 2006 and the other in summer 2010.
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They also influenced air quality in Finland (Leino et al.,
2014). These fires providetemporally and spatially well-defined
sources that can be used to evaluate the ability ofthe trajectory
analysis to identify the source areas.
Biomass burning has previously been observed to be a source of
VOCs in severalfield and laboratory studies (e.g. Crutzen and
Andreae, 1990; Holzinger et al., 1999;5de Gouw et al., 2006;
Virkkula et al., 2014). Acetonitrile has commonly been used asa
marker compound for emissions from biomass burning (de Gouw et al.,
2003, 2006;Holzinger et al., 1999). The oxygenated VOCs (OVOCs) and
aromatic VOCs (benzeneand toluene) have also been linked to biomass
burning in different studies (Koppmannand Wildt, 2007; de Gouw et
al., 2006). Forest fires affect air quality; and the biggest10smoke
plumes can be seen in satellite images and even reduce visibility
in the plumeareas.
Forest fires, which were observed during these measurements,
occurred in the year2006 in the Vyborg area near the FinnishRussian
border and in year 2010 in theMoscow area (Fig. 2). The sum of all
observed fires during the two episodes are shown15in Fig. 2; in
order to be visible on the map, the forest at the given location
was not neces-sarily burning all the time. Lappalainen et al.
(2009) observed an exceptional drought atSMEAR II during summer of
2006. During the summer of 2010 an unusually high tem-perature
anomaly was observed in Eastern Europe (Twardosz and
Kossowska-Cezak,2013). Williams et al. (2011) also reported
unusually high temperatures at SMEAR II20during summer 2010. These
circumstances can be favourable for the ignition and de-velopment
of major forest fires.
In summer 2006 the largest fires were south of Moscow and in
Belarus. There werealso fires in Karelia, in Vyborg area near St.
Petersburg and the Finnish-Russian bor-der. The first forest fire
episode occurring within the time-frame of this study was in
the25period 431 August 2006. The trajectory analysis shows the high
VOC mixing ratiosobserved at SMEAR II during this period to have
originated from that area (Fig. 3). Un-fortunately, acetonitrile
was not being measured during this period. During this
periodAnttila et al. (2008) also observed elevated amounts of
particulate matter mass (PM10,
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PM2.5) and higher polycyclic aromatic hydrocarbon (PAH)
concentrations at Virolahti,located on the Finnish south coast near
the Finnish-Russian border.
During summer 2010 a large number of forest fires were located
in Northwest Russia.The second forest fire episode within the time
frame of this study was in 20 July31 Au-gust 2010. During this
period acetonitrile data too was being measured at SMEAR
II.5Comparing the forest fire locations (Fig. 2) and acetonitriles
mean VMR field from tra-jectory analysis, one can see that
acetonitrile is originating from the general directionof the
maximum burning area (Fig. 4). All the other VOCs and trace gases
studied alsohad similar source area distributions to
acetonitrile.
For comparison between fire and non-fire VOC VMRs, the mean VMR
values of10methanol, acetonitrile, benzene, SO2 and CO were
calculated for periods both beforeand during the forest fire
episodes in both years (Table 5). In, 2006 the period from 1to 31
July 2006 was selected to represent the situation before the forest
fires. In, 2010the period from 25 June to 15 July 2010 was used for
the same purpose. In both casesconcentrations were calculated using
data, filtered by trajectories, of air which had15traversed the
burning areas. In this study, the forest fire areas were selected
to be [2736 E, 5862N] (area 1) in 2006 and [2834 E, 5661N] (area 2)
in 2010. Before theforest fire episode in year 2010, the VMR of
acetonitrile was 0.06 ppbv, while duringthe episode it was 0.13
ppbv. Benzene too had elevated VMR levels in arriving airmasses
that had travelled over forest fires. In, 2006, benzenes VMR before
the forest20fires was 0.08 ppbv and during the episode 0.17 ppbv;
in 2010 the values were 0.05and 0.09 ppbv, respectively.
Acetonitrile and benzene have earlier been observed to beemitted
from biomass burning (Holzinger et al., 1999; Simpson et al., 2011;
Virkkulaet al., 2014). Elevated CO concentrations were also
observed in air masses associatedwith forest fires. Before the
forest fire episodes, CO was measured at 128 ppbv in 200625and 111
ppbv in 2010. During the episodes, CO concentrations were 154 and
152 ppbv,respectively. However, the mean VMRs of methanol did not
increase during the fireepisodes, which indicates that methanol at
SMEAR II has other dominant sources, suchas biogenic emissions from
vegetation. SO2 is usually linked to fossil fuel combustion
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processes rather than biomass burning (Seinfeld and Pandis,
1998). In this study, too,the SO2 concentration in 2006 was
actually lower during the forest fire episode thanbefore it; in
2010 it was only slightly elevated during the episode.
3.3 Differences in source areas for the whole measurement
period
The mean trajectory fields of the VOCs studied (methanol,
acetonitrile, acetaldehyde,5acetone, benzene and toluene), as well
as CO, NOx and SO2 from the year 2006 to theyear 2011 are presented
in Fig. 5. From this figure it can be seen that methanol, ace-tone
and acetaldehyde had very similar source areas. There was very good
correlationbetween methanol and acetone (r = 0.86, p = 0). Methanol
and acetaldehyde werealso correlated to each other (r = 0.66, p =
0). On the other hand, the source areas10of benzene, toluene, CO
and NOx were also similar to each other and correlated
well.Acetonitrile had similarities with both methanol and benzene,
correlating, however, bet-ter with the methanol group than with the
benzene group. The correlation matrix andPearsons correlation
coefficients between all compounds are presented in Fig. A1. Allp
values were below 0.05 except those for the correlations between
methanol and ben-15zene, NOx and CO which were statistically
insignificant. Anthropogenically-influencedsource areas (benzene,
toluene, NOx, CO) were observed in the northern part of
Con-tinental Europe and Eastern Europe, Fennoscandia, Western
Russia, and marine andcoastal areas (Baltic Sea, Barents Sea, White
Sea, Norwegian Sea and North Sea)(Fig. 4). Northern Fennoscandia
seemed to be quite free of anthropogenic sources of20VOCs. NOx, CO
and SO2 concentrations were also low in air masses arriving fromthe
north. Except methanol and acetonitrile, the other VOCs do not have
any elevatedsource areas in the Northern area. The occurrence of
forest fires in Eastern Europe isvisible in the mean trajectory
fields of all data. In addition to biomass burning, somesmall local
emissions e.g. from traffic, wood combustion and biogenic emissions
may25also have had an effect on the results.
The source areas of SO2 were similar to those of NOx and CO. The
main source ar-eas of SO2 were Western Russia, Northern Poland and
Kaliningrad, while NOx and CO
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had additional source areas in Western Europe. Riuttanen et al.
(2013) found a similarsharp distinction in SO2 concentrations
between air masses arriving from Eastern andWestern Europe. They
also found that air masses coming from Central Europe wereexposed
to more rain and thus wet removal of SO2. They speculate that this
may beone reason why SO2 source areas in Central Europe were not
separable in the tra-5jectory fields. However, in this study we
found that water-soluble methanol and alsoNOx were not totally
washed away, as there were visible source areas for methanolin the
North Sea, Skagerrak and the northern Germany areas, and for NOx in
North-ern Continental Europe (Fig. 5). We propose that wet
deposition does not fully explainthe absence of SO2 in the air
masses arriving from Western Europe, as interpreted10by Riuttanen
et al. (2013), but that the observed difference in SO2 is probably
due tomore rigorous emission regulations in the western part of
Europe than the eastern part.This interpretation is supported by
Vestreng et al. (2007), who observed a difference inthe reduction
of emissions of SO2 between Western and Eastern Europe during
years19802004. According to the EMEP database SO2, emissions in the
European Union15have decreased from about 25.8 to 4 Gg over the
period 19902012. Emission datafrom Eastern Europe for the same time
period is scarce. Riuttanen et al. (2013) alsoobserved a general
decreasing trend of SO2 of 5.2 % per year at SMEAR II
during19972008. Anttila and Tuovinen (2010) observed a decreasing
trend of SO2 of 2.2 %per year for the whole of Finland during the
years 19942006.20
3.4 Source areas and their seasonal difference
In order to study the possible seasonal changes in the VOC
source areas, thesewere determined separately for the summer
(JuneAugust) and the winter (December-February) periods. Data of
the short forest fire episodes were removed prior to thisanalysis
so that they would not mask other source areas. Although there was
some25inter-annual variation in the observed VMRs of VOCs, no clear
trends of VMRs wereobserved during the whole measurement period
(Fig. 1). The long-term trends of se-lected VOCs based on the
summertime medians of the VMRs of VOCs studied are
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presented in Table 6. These trends are all slightly negative
except for that of monoter-penes that seemed to be slightly
positive. For monoterpenes the VMR change was8 % per year for the
summer monthly medians. However, one should interpret thesetrends
with care, because they were calculated based on measured summer
monthlymedians whose trends were not, in fact, statistically
significant (i.e. the confidence5intervals included zero). Summers
were different from each other, e.g. the median tem-perature
between summers in Finland at Hyytil were observed to vary from
12.9to 17 C, which may have an effect on the emissions of VOCs e.g.
methanol, acetoneand monoterpenes. Additionally, in the case of
monoterpenes VMRs the change of thesampling location may bias the
trend calculated over the whole observation period,
as10monoterpenes have a short life time (1 h; Table 4).
The source areas of the VOCs also varied slightly between years
due to variationsin the VOC VMRs, deposition and the prevailing
paths of arriving air masses. Despitethese differences, all five
summers (20062008, 20102011) and two winters (20062007 and
20082009) were combined in this study, to get as good an areal
trajectory15data coverage as possible for summers and winters
separately. Summer, 2009 was notincluded in the trajectory analysis
because VOC VMR data was then only available for18 days.
For the evaluation of the VMR source areas, ten rectangular
areas were selected forseparate analysis of the trajectory fields
of both main seasons (summer, winter) (Fig. 6).20These areas differ
from each other in having e.g. different industry and
populationdensities.
The ten selected areas were as follows: (1) Western Russia
[5461N, 2940 E]. Inthis area there are two densely-populated
cities, Moscow and St. Petersburg, and alsoharbours on the Baltic
Sea. The area includes many different industries as described25in
detail in Table A1, (2) Northern Poland, Kaliningrad and Baltic
countries (Estonia,Latvia and Lithuania) [5359N, 1828 E]. This area
includes the port of Gdansk,which is one of the important harbors
in the Baltic Sea. Similarly to Western Russiathere is also a lot
of industry in this area, (3) Karelia and the White Sea [6366N,
31
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40 E]. This area is a significant Russian industrial area, also
including large forestsand the coastal areas of White Sea. Compared
to the Western Russia source area,the Karelia and White Sea area is
more sparsely populated, (4) The Kola Peninsulaand Barents Sea
[6670N, 2942 E]. This area is rich in minerals and is therefore
animportant Russian industrial area. Around the Barents Sea there
is an active petroleum5industry (Austvik, 2007) with several oil
drilling sites and oil tankers present on the Nor-wegian and
Russian coasts. Four different source areas for VOCs were then
defined inFennoscandia (Denmark, Finland, Norway and Sweden): (5)
The Bay of Bothnia [6467N, 2025 E] was separated off as its own
source area, as there are several metal-lurgy plants there
(Skellefte, Lule, Tornio, Raahe) and also wood and timber
industry,10(6) The coast of the Norwegian Sea and Northern Sweden
[6769N, 1320 E]. In thisarea the notable cities in industrial point
of view are Kiruna and Narvik. Kiruna has richiron ore deposits and
Narvik has a large port, (7) The Stockholm region [5861N,1520 E] is
one of the most important industrial areas in Sweden. Stockholm is
alsothe largest city in Fennoscandia, with an important harbour,
(8) The Skagerrak area15[5460N, 814 E] includes the surroundings of
Goteborg, Malm, Copenhagen andOslo. In addition to the Stockholm
area, the Skagerrak region is also significant froman industrial
point of view. There are many important ports and diverse industry
in thisarea, (9) In the North Sea and coastal areas [5258N, 16 E]
are situated Europesimportant offshore oil and gas fields. Norway,
Denmark, Germany, the Netherlands and20the UK are involved in oil
production in the North Sea (EIA, International Energy Out-look,
2014). The coastal areas of the North Sea are also very densely
populated; (10)Northern Germany [5254N, 815 E]. This is one of
Germanys main industry areas,with several important harbours such
as Hamburg, Lbeck and Rostock.
To evaluate the source areas for the studied VOCs during summer
and winter, two25different VOC VMR trajectory fields were
calculated for each compound. The momen-tary VOC VMR trajectory
field was calculated directly from the measured one-hourmedian
values. The interpolated VOC VMR trajectory field was calculated
from a lin-ear interpolation of the monthly median values
corresponding to the same time period.
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This interpolated VOC VMR trajectory field is called the
background field in the follow-ing discussion. By subtracting the
latter from the momentary field a differential sourcefield was
obtained, with the seasonal trend removed from the source area
analysis.The average values of the differential source field for
the ten areas listed above arepresented in Fig. 7. Acetonitrile was
included in the source area analysis in summer5only, when forest
fires occurred.
In urban areas, VOCs mostly originate from traffic, but are also
evaporated from fuelsand combustion processes (Reimann and Lewis,
2007; Helln et al., 2006) and fromvarious industrial processes. The
ten areas investigated here were mainly located insimilar areas
having a lot of industry and/or dense population. The strongest
source10areas for all the VOCs studied were located in Eastern
Europe including Western Rus-sia, Northern Poland, Kaliningrad and
the Baltic countries, Karelia and the White Sea.In these areas,
calculated mean VMR values differed significantly from values
basedon monthly medians (Fig. 7). In addition to these source
areas, which are common tomost of the studied compounds, certain
compounds have specific source areas of their15own. Methanol is
known to be a very abundant VOC in the atmosphere having
manydifferent sources, both biogenic and anthropogenic (Jacob et
al., 2005). From Fig. 7,it can be seen that nearly all of the
selected areas are sources of methanol. EasternEurope was observed
to be a large emitter of OVOCs (methanol, acetaldehyde andacetone)
in general. This is in line with earlier observations by Helln et
al. (2004), who20reported Eastern Europe to be an important emitter
of carbonyls. In addition to thesesource areas, acetone and
acetaldehyde also arrived from the areas of Stockholm,
theSkagerrak, the North Sea and the coastal areas and northern
Germany: all these areashave traffic emissions and solvent use
related to various different industries. The oxi-dation of
hydrocarbons and the primary biogenic emission of acetaldehyde are
known25to be the major global source of acetaldehyde (Singh et al.,
2004); these sources havenot, however, been taken to account into
this study. Acetaldehyde also has relativelyshort lifetime (2 days)
during summer. These factors can add some uncertainty to
thisanalysis of acetaldehyde. Benzene was also found to have
sources in the Kola Penin-
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sula area, connected with the petrochemical industry and mining.
However, the NorthSea area with its active petrochemical industry
did not appear as a distinguishablesource area for aromatic VOCs in
this study.
As seen earlier, when comparing the VMRs of VOCs before and
during forest fires,the mixing ratios at SMEAR II for benzene and
acetonitrile were found to be elevated5during these episodes (Table
5). In Fig. 7, forest fires seem to be sources of all VOCs,and
especially for acetonitrile. Acetonitriles VMR difference from the
background fieldvalues was in the year 2010 0.07 ppbv. In other
source areas, acetonitriles VMR differ-ence from values based on
the background field was indistinguishable (Fig. 7).
The strongest source areas in the trajectory maps seemed to be
of anthropogenic10origin, and eight out of ten were located in an
easterly or southerly direction fromFinland. However, there is a
vast boreal forest zone in Northern Europe that is an im-portant
emitter of biogenic VOCs (BVOCs). Most BVOCS emitted from boreal
forestsare short-lived terpenoids, whose high atmospheric
reactivity keeps their concentra-tions relatively low (Hakola et
al., 2003) compared to those of e.g. OVOCs, with
longer15atmospheric lifetimes. In this study, the forest regions
were not identifiable as well-defined source areas, but probably
contributed to background levels. During summerthere were minor
source areas in the Baltic Sea, where there should be no
anthro-pogenic sources. These interesting source areas could be
producing VOCs from e.g.algae or cyanobacteria. However, the VMR
levels of VOCs originating from algae are20low (Kansal et al.,
2009) compared to anthropogenic sources. With the current data
andanalysis it is not possible to identify the source of these
marine emissions. In the future,their origin could be clarified by
using shorter trajectories and making measurementsnear the Baltic
Sea or by collecting samples from over the Baltic Sea.
3.5 Low concentrations from the north, urban influences from
continents and25seas
VOC sources were analyzed with the Unmix 6.0 receptor model. For
the Unmix analy-sis, VOC VMR and trace gas data were divided into
three sectors according to the wind
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direction at SMEAR II. The division was made based on the
findings of the VOC sourceareas described above. The sectors were:
(1) North (05 and 300360), Urbanizedcontinental (5210) and Urban
and sea (210300). In all sectors, three distinctivesources were
identified: (1) A source containing mainly SO2; this was named the
SO2source, (2) A source containing toluene, benzene, NOx and CO.
These compounds are5typical of anthropogenic emissions, and thus
the source was named the anthropogenicsource, (3) A source
containing oxygenated VOCs (OVOCs, methanol, acetone
andacetaldehyde), acetonitrile and a portion of CO. This source was
related to biomassburning and other biogenic emissions, and was
named to biogenic/combustion source.
The mean contributions of all these sources in the different
sectors were similar10in both seasons. The contribution of the
anthropogenic source was dominant in win-ter and the
biogenic/combustion source in summer (Fig. 8). The dominance of
thebiogenic/combustion source in summer can be attributed to two
processes. First, thebiogenic/combustion source included
acetonitrile, and high mixing ratios of acetoni-trile were observed
during forest fire episodes in summer; secondly, this source
in-15cluded OVOCs which also have biogenic sources and thus higher
VMRs during sum-mer (Fig. 1). Biogenic emissions are dominant at
SMEAR II in summer. The monoter-penes measured at SMEAR II are
mostly emitted from biogenic sources. However,from the time series
of monoterpenes it can be seen that there are occasionally no-tably
high VMR peaks, which are known to have an anthropogenic origin
(Liao et al.,202011) (Fig. 1). Monoterpenes were not included in
the trajectory and Unmix analysisbecause they differ from the other
VOCs in this study in having a short lifetime fromone hour to
several hours and mainly local sources. A comparison of diurnal
cyclesof the anthropogenic source and monoterpenes is presented in
Fig. 9. Data in Fig. 9are from the urbanized continental sector.
Monoterpenes had a considerable diurnal25variation during summer,
with higher mixing ratios at night, and no variation in winter,as
also observed in previous studies in similar ecosystems (Hakola et
al., 2000; Rinneet al., 2005). This is due to the diurnal cycle in
surface layer mixing and the night-timeemissions of monoterpenes
from coniferous trees. The aromatic VOCs have shorter
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summertime lifetimes as compared to winter, leading to lower
anthropogenic sourcelevels in summer. The contribution of the
anthropogenic source in winter was aboutthree times higher in all
sectors than in summer. Both summertime and wintertime di-urnal
cycles of the anthropogenic source show a maximum at night,
possibly due tolower night-time mixing in the boundary layer.5
Histograms of the source contributions, together with their
mean, median and max-imum values both in summer and in winter, are
presented in Figs. 10 and 11. Manyof these distributions are
skewed, having a tail of high contribution values. Thus themean and
median values of these source contributions may have large
differences.The skewness of the source distributions also indicates
that the simplest statistical pa-10rameters, such as mean and
median, may not adequately describe the distribution ofthe sources
or their contribution to the local atmospheric mixing ratios of
these com-pounds. There were considerable differences between the
source distributions fromdifferent wind direction sectors. Air
masses arriving from the North sector had in gen-eral lower source
contributions than air masses from the Urbanized continental
and15the Urban and sea sectors. Particularly in summer there was a
tail of high contributionsin the Urbanized continental and the
Urban and sea sectors for anthropogenic (maxi-mum values were
17.2/4.7) and biogenic/combustion sources (9.8/6.1), as comparedto
the North sector whose maximum contributions were 1.6 and 4.9 for
the anthro-pogenic and biogenic/combustion sources, respectively.
Hence air masses from the20north were clearly less polluted with
the trace gases studied as compared to the twoother sectors. These
results combined with the earlier observations in this paper
sup-port the conclusion that air masses related to the highest VMRs
of long-lived VOCsobserved at SMEAR II have their origin in Russia
and the Eastern European countries,the Northern part of Continental
Europe and Southern and Central Fennoscandia.25
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4 Conclusions
This study has focused on identifying the source areas of the
long-lived VOCs(methanol, acetonitrile, acetaldehyde, acetone,
benzene) measured at the SMEAR IIsite in southern Finland, and to
investigate the relative influences of biogenic and an-thropogenic
compounds arriving in Southern Finland from areas outside the
country.5The analysis is based on a data set which consisted of
several years (20062011) ofVMR measurements of OVOCs, acetonitrile,
aromatic VOCs and monoterpenes. An-nual trends of VOC VMRs based on
summer monthly medians were presented for themeasurement period.
The trend of VMRs for monoterpenes was slightly positive. Allthe
other VOCs had a small negative trend in their VMRs. Trend
calculations showed,10however, that none of these trends could be
considered as significant. The origin andsources of the VOCs
observed were analyzed by trajectory model and a
multivariatereceptor model.
During the measurement period, forest fire episodes occurred in
Eastern Europe andRussia. Elevated VMR levels for several VOCs and
other trace gases were observed15in air masses arriving from areas
in which abundant fire counts were observed. Thiscorroborates the
applicability of the trajectory analysis as a method for
identifying thesource areas of these trace gases.
Three sources (labelled SO2, biogenic/combustion and
anthropogenic) were sepa-rated by receptor analysis both in winter
and summer. The biogenic/combustion source20dominated in summer and
the anthropogenic source in winter. Both the trajectory andUnmix
analyses showed that air masses coming from a northerly direction
were lesspolluted with the trace gases studied than the air-masses
arriving from easterly andwesterly directions.
Ten different source area regions were selected for further
analysis. All the source25areas seemed to have enhanced emissions
due to anthropogenic activity: most of theareas contained with
abundant industry. There were some differences in the impor-tance
of these source areas between summer and winter. Western Russia,
Northern
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Poland, Kaliningrad and the Baltic regions and Karelia turned
out to be the most sig-nificant source area for all the VOCs.
Benzene came mainly from areas related to thepetrochemical
industry, such as the Kola Peninsula, while acetone and
acetaldehydewere related to areas where solvents are used in
industry e.g. the Skagerrak and NorthGermany. Forest fire areas
stood out clearly as sources for all the VOCs studied
and5especially for acetonitrile. Even though boreal forests, with
their high OVOC emissions,covered large areas in the region, these
forest areas were not specifically indicated assource areas.
However, they probably did contribute to the regional background
lev-els. The level of SO2 concentration showed a clear difference
between Eastern andWestern European source areas, which was not
seen in water soluble VOCs and thus,10contributed to reduction in
emissions. Some biogenic influence appeared in the BalticSea region
in summer. One potential VOC emitter there could be algae or
cyanobacte-ria, which would be worth studying in the future.
Acknowledgements. This work was financially supported by the
Academy of Finland Centreof Excellence program (projects 1118615
and 272041) and the Nordic Centre of Excellence15CRAICC. The
authors thank H. Helln for helping in the photolysis rate
calculations. NASAMODIS is acknowledged for providing data at data
server.
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Table 1. Detection limits (ppbv) for one hour averages of the
measured VOCs during the mea-surement periods. Percentages of
values below detection limit are presented in brackets.
VOC DL1 DL2 DL3 DL4(12 Jun (29 Nov 2006 (12 Jul 2007 (28 May
201026 Sep 2006) 10 Jul 2007) 22 Jun 2009) 29 Dec 2011)
methanol 0.07 (1 %) 0.06 (1 %) 0.07 (1 %) 0.1 (3 %)acetonitrile
0.004 (1 %) 0.005 (4 %)acetaldehyde 0.02 (2 %) 0.02 (1 %) 0.02 (1
%) 0.03 (1 %)acetone 0.02 (1 %) 0.01 (1 %) 0.02 (1 %) 0.03 (1
%)benzene 0.005 (3 %) 0.003 (1 %) 0.005 (3 %) 0.006 (2 %)toluene
0.02 (73 %) 0.01 (1 %) 0.02 (22 %) 0.02 (4 %)monoterpenes 0.01 (3
%) 0.01 (19 %) 0.01 (4 %) 0.02 (12 %)
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ACPD15, 1459314641, 2015
Sources of long-livedatmospheric VOCs atthe rural boreal
forest
site, SMEAR II
J. Patokoski et al.
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Table 2. Concentrations of hydroxyl (OH), ozone (O3), and
nitrate radicals (NO3) used in thelifetime calculations of the
VOCs.
Oxidants winter [moleculescm3] summer [moleculescm3]
[OH] day 5.5104 a 1.5106 a[O3] day/night 6.81011/5.71011
8.61011/7.11011[NO3] night 1.2107 a 4.2107 a
a Hakola et al. (2003).
14624
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ACPD15, 1459314641, 2015
Sources of long-livedatmospheric VOCs atthe rural boreal
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Table 3. Reaction rate coefficients (kOH, kO3 , kNO3 ) and
photolysis rates for the measured VOCs.
kOH kO3 kNO3 kphotolysis kphotolysis[cm3 molecules1 s1] [cm3
molecules1 s1] [cm3 molecules1 s1] in winter [s1] in summer
[s1]
methanol 9.001013 a 2.421016 dacetaldehyde 1.501012 a 2.721015 e
1.50106 f 3.27106 facetone 1.801013 a 3.001017e 2.32107 f 4.85107
fbenzene 1.191012 a 1.701022 c 3.001017 dtoluene 5.601012 a
4.101022 c 6.791017 dmonoterpenes 7.501011 b 1.41017 b 7.061012
b
Rate constants (kOH, kO3 and kNO3 ) used in calculations in
Table 4: (a) iupac preferred, (b) Monoterpenes rate constants kOH,
kO3 and kNO3 werecalculated as weighted averages of individual
monoterpenes typical in SMEAR II (Hakola et al., 2003), individual
k values (Atkinson, 1994), (c) Atkinson,1994, (d)
http://kinetics.nist.gov/kinetics/Search.jsp, last access: 17
January 2013, (e) Rinne et al. (2007), (f) Calculated similar to
Helln et al. (2004).
14625
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ACPD15, 1459314641, 2015
Sources of long-livedatmospheric VOCs atthe rural boreal
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site, SMEAR II
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Table 4. Total atmospheric lifetimes (e-folding times) of the
VOCs studied, daytime and night-time in summer and winter. Daytime
values are the sums of lifetimes calculated towards O3,OH and
photolysis. Night-time values were calculated towards O3 and
NO3.
VOC total lifetimes on total lifetimes on total lifetimes on
total lifetimes ona winter day a winter night a summer day a summer
night
methanol 234 d 1 y 9 d 113 dacetaldehyde 7 d 1 y 2 d 101
dacetone 48 d 88 y 15 d 25 ybenzene 177 d 69 y 6 d 27 ytoluene 38 d
29 y 1 d 11 ymonoterpenes 1 d 3 h 1 h 0.9 h
14626
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ACPD15, 1459314641, 2015
Sources of long-livedatmospheric VOCs atthe rural boreal
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site, SMEAR II
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Table 5. Mean VMRs of studied trace gases (ppbv) and SDs (std)
before and during the forestfire episodes in 2006 and 2010. The
VMRs of the compounds were calculated from VMR datawhich were
selected using trajectories from the burning areas. Area 1 is [2736
E, 5862 N]in 2006 and area 2 is [2834 E, 5661 N] in 2010.
Compounds in 2006 std in 2006 std in 2010 std in 2010 stdbefore
during before duringepisode episode episode episode
methanol 4.94 0.78 3.21 1.21 3.74 0.75 3.69 2.10acetonitrile
0.06 0.01 0.13 0.07benzene 0.08 0.03 0.17 0.13 0.05 0.03 0.09
0.06sulphur dioxide 0.23 0.14 0.16 0.15 0.20 0.12 0.35 0.42carbon
monoxide 128 5 154 49 111 9 152 45
14627
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ACPD15, 1459314641, 2015
Sources of long-livedatmospheric VOCs atthe rural boreal
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Table 6. Long-term trends of VOC VMRs calculated from summer
monthly medians. A linearregression line is fitted to the summer
monthly medians (Fig. 1) during the years 20062011.June 2006 (for
acetonitrile July 2007) was used as the reference year when
calculating therelative trend. The confidence intervals of the
calculated trends included zero, thus the trendsare not
statistically significant.
Compound Change per year [%]
methanol 4acetonitrile 2acetaldehyde 2acetone 2benzene
8monoterpenes 8
14628
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ACPD15, 1459314641, 2015
Sources of long-livedatmospheric VOCs atthe rural boreal
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Table A1. Main industries of source areas.
Source area Main industries
1. Western Russia Oil and gas trade, shipbuilding yards, machine
building, heavymachinery, mining, ferrous and nonferrous
metallurgy, chemicalindustry and energy and paper production1
2. Northern Poland, Machinery and chemical industry: chemicals,
petroleum and refining,Kaliningrad and shipbuilding and coal
mining2, forestry with wood and processed woodBaltic countries
products, chemical, pharmaceuticals, plastic and rubber industry,
metal
and electronics industry3
3. Karelia and Forest industry, ferrous and non-ferrous
metallurgy, coastal areas ofWhite Sea the White Sea: oil production
and processing4
4. Kola Peninsula Mining, iron industry (iron-ore enterprises
and separators), apatiteand Barents Sea production and other metal
industry such as aluminum and nickel
plants and smelters5, petroleum industry6
5. Bay of Bothnia Metallurgy